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Abstract Sustainable polymers from renewable resources have been gaining importance due to their recyclability and reduced environmental impact. However, their development through conventional trial‐and‐error methods remains inefficient and resource‐intensive. Machine learning (ML) has emerged as a powerful tool in polymer science, enabling rapid prediction, and discovery of new chemicals and materials. In this review, we examine emerging trends in ML applications for sustainable polymer development, focusing on catalyst discovery, property optimization, and new polymer design. We analyze unique challenges in applying ML to sustainable polymers and evaluate proposed solutions, providing insights for future development in this rapidly evolving field.
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The Rise of Machine Learning in Polymer Discovery
In the recent decades, with rapid development in computing power and algorithms, machine learning (ML) has exhibited its enormous potential in new polymer discovery. Herein, the history of ML is described and the basic process of ML accelerated polymer discovery is summarized. Next, the four steps in this process are reviewed, that is, dataset selection, fingerprinting, ML framework, and new polymer generation. Finally, a couple of main challenges for ML accelerated polymer discovery is presented and the outlooks in this field are prospected. It is expected that this review can service as a useful tool for the people who just step into this field and deepen the understanding for the people who are already in this field.
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- PAR ID:
- 10394821
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Intelligent Systems
- Volume:
- 5
- Issue:
- 4
- ISSN:
- 2640-4567
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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